source: sasview/DataLoader/qsmearing.py @ fa1b4ad

#####################################################################
#This software was developed by the University of Tennessee as part of the
#Distributed Data Analysis of Neutron Scattering Experiments (DANSE)
#project funded by the US National Science Foundation. 
#See the license text in license.txt
#copyright 2008, University of Tennessee
######################################################################

import DataLoader.extensions.smearer as smearer
import numpy
import math
import logging, sys
from DataLoader.smearing_2d import Smearer2D

def smear_selection(data1D):
    """
    Creates the right type of smearer according 
    to the data.

    The canSAS format has a rule that either
    slit smearing data OR resolution smearing data
    is available. 
    
    For the present purpose, we choose the one that
    has none-zero data. If both slit and resolution
    smearing arrays are filled with good data 
    (which should not happen), then we choose the
    resolution smearing data. 
    
    :param data1D: Data1D object
    """
    # Sanity check. If we are not dealing with a SANS Data1D
    # object, just return None
    if  data1D.__class__.__name__ != 'Data1D':
        if data1D == None:
            return None
        elif data1D.dqx_data == None or data1D.dqy_data == None:
            return None
        return Smearer2D(data1D)
    
    if  not hasattr(data1D, "dx") and not hasattr(data1D, "dxl") and not hasattr(data1D, "dxw"):
        return None
    
    # Look for resolution smearing data
    _found_resolution = False
    if data1D.dx is not None and len(data1D.dx)==len(data1D.x):
        
        # Check that we have non-zero data
        if data1D.dx[0]>0.0:
            _found_resolution = True
            #print "_found_resolution",_found_resolution
            #print "data1D.dx[0]",data1D.dx[0],data1D.dxl[0]
    # If we found resolution smearing data, return a QSmearer
    if _found_resolution == True:
        return QSmearer(data1D)

    # Look for slit smearing data
    _found_slit = False
    if data1D.dxl is not None and len(data1D.dxl)==len(data1D.x) \
        and data1D.dxw is not None and len(data1D.dxw)==len(data1D.x):
        
        # Check that we have non-zero data
        if data1D.dxl[0]>0.0 or data1D.dxw[0]>0.0:
            _found_slit = True
        
        # Sanity check: all data should be the same as a function of Q
        for item in data1D.dxl:
            if data1D.dxl[0] != item:
                _found_resolution = False
                break
            
        for item in data1D.dxw:
            if data1D.dxw[0] != item:
                _found_resolution = False
                break
    # If we found slit smearing data, return a slit smearer
    if _found_slit == True:
        return SlitSmearer(data1D)
    
    return None
            

class _BaseSmearer(object):
    
    def __init__(self):
        self.nbins = 0
        self._weights = None
        ## Internal flag to keep track of C++ smearer initialization
        self._init_complete = False
        self._smearer = None
        
    def __deepcopy__(self, memo={}):
        """
        Return a valid copy of self.
        Avoid copying the _smearer C object and force a matrix recompute
        when the copy is used.  
        """
        result = _BaseSmearer()
        result.nbins = self.nbins
        return result

        
    def _compute_matrix(self): return NotImplemented

    def get_bin_range(self, q_min=None, q_max=None):
        """
        
        :param q_min: minimum q-value to smear
        :param q_max: maximum q-value to smear
         
        """
        # If this is the first time we call for smearing,
        # initialize the C++ smearer object first
        if not self._init_complete:
            self._initialize_smearer()
            
        if q_min == None:
            q_min = self.min
        
        if q_max == None:
            q_max = self.max
        
        _qmin_unsmeared, _qmax_unsmeared = self.get_unsmeared_range(q_min, q_max)
        
        _first_bin = None
        _last_bin  = None

        step = (self.max-self.min)/(self.nbins-1.0)
        try:
            for i in range(self.nbins):
                q_i = smearer.get_q(self._smearer, i)
                if (q_i >= _qmin_unsmeared) and (q_i <= _qmax_unsmeared):
                    # Identify first and last bin
                    if _first_bin is None:
                        _first_bin = i
                    else:
                        _last_bin  = i
        except:
            raise RuntimeError, "_BaseSmearer.get_bin_range: error getting range\n  %s" % sys.exc_value
               
        return _first_bin, _last_bin

    def __call__(self, iq_in, first_bin=0, last_bin=None):
        """
        Perform smearing
        """
        # If this is the first time we call for smearing,
        # initialize the C++ smearer object first
        if not self._init_complete:
            self._initialize_smearer()
             
        # Get the max value for the last bin
        if last_bin is None or last_bin>=len(iq_in):
            last_bin = len(iq_in)-1
        # Check that the first bin is positive
        if first_bin<0:
            first_bin = 0
            
        # Sanity check
        if len(iq_in) != self.nbins:
            raise RuntimeError, "Invalid I(q) vector: inconsistent array length %d != %s" % (len(iq_in), str(self.nbins))
             
        # Storage for smeared I(q)   
        iq_out = numpy.zeros(self.nbins)
        smear_output = smearer.smear(self._smearer, iq_in, iq_out, first_bin, last_bin)
        if smear_output < 0:
            raise RuntimeError, "_BaseSmearer: could not smear, code = %g" % smear_output
        return iq_out
    
class _SlitSmearer(_BaseSmearer):
    """
    Slit smearing for I(q) array
    """
    
    def __init__(self, nbins=None, width=None, height=None, min=None, max=None):
        """
        Initialization
            
        :param iq: I(q) array [cm-1]
        :param width: slit width [A-1]
        :param height: slit height [A-1]
        :param min: Q_min [A-1]
        :param max: Q_max [A-1]
        
        """
        _BaseSmearer.__init__(self)
        ## Slit width in Q units
        self.width  = width
        ## Slit height in Q units
        self.height = height
        ## Q_min (Min Q-value for I(q))
        self.min    = min
        ## Q_max (Max Q_value for I(q))
        self.max    = max
        ## Number of Q bins 
        self.nbins  = nbins
        ## Number of points used in the smearing computation
        self.npts   = 1000
        ## Smearing matrix
        self._weights = None
        self.qvalues  = None
        
    def _initialize_smearer(self):
        """
        Initialize the C++ smearer object.
        This method HAS to be called before smearing
        """
        #self._smearer = smearer.new_slit_smearer(self.width, self.height, self.min, self.max, self.nbins)
        self._smearer = smearer.new_slit_smearer_with_q(self.width, self.height, self.qvalues)
        self._init_complete = True

    def get_unsmeared_range(self, q_min, q_max):
        """
        Determine the range needed in unsmeared-Q to cover
        the smeared Q range
        """
        # Range used for input to smearing
        _qmin_unsmeared = q_min
        _qmax_unsmeared = q_max 
        try:
            _qmin_unsmeared = self.min
            _qmax_unsmeared = self.max
        except:
            logging.error("_SlitSmearer.get_bin_range: %s" % sys.exc_value)
        return _qmin_unsmeared, _qmax_unsmeared

class SlitSmearer(_SlitSmearer):
    """
    Adaptor for slit smearing class and SANS data
    """
    def __init__(self, data1D):
        """
        Assumption: equally spaced bins of increasing q-values.
        
        :param data1D: data used to set the smearing parameters
        """
        # Initialization from parent class
        super(SlitSmearer, self).__init__()
        
        ## Slit width
        self.width = 0
        if data1D.dxw is not None and len(data1D.dxw)==len(data1D.x):
            self.width = data1D.dxw[0]
            # Sanity check
            for value in data1D.dxw:
                if value != self.width:
                    raise RuntimeError, "Slit smearing parameters must be the same for all data"
                
        ## Slit height
        self.height = 0
        if data1D.dxl is not None and len(data1D.dxl)==len(data1D.x):
            self.height = data1D.dxl[0]
            # Sanity check
            for value in data1D.dxl:
                if value != self.height:
                    raise RuntimeError, "Slit smearing parameters must be the same for all data"
        
        ## Number of Q bins
        self.nbins = len(data1D.x)
        ## Minimum Q 
        self.min = min(data1D.x)
        ## Maximum
        self.max = max(data1D.x)
        ## Q-values
        self.qvalues = data1D.x
        

class _QSmearer(_BaseSmearer):
    """
    Perform Gaussian Q smearing
    """
        
    def __init__(self, nbins=None, width=None, min=None, max=None):
        """
        Initialization
        
        :param nbins: number of Q bins
        :param width: array standard deviation in Q [A-1]
        :param min: Q_min [A-1]
        :param max: Q_max [A-1]
        """
        _BaseSmearer.__init__(self)
        ## Standard deviation in Q [A-1]
        self.width  = width
        ## Q_min (Min Q-value for I(q))
        self.min    = min
        ## Q_max (Max Q_value for I(q))
        self.max    = max
        ## Number of Q bins 
        self.nbins  = nbins
        ## Smearing matrix
        self._weights = None
        self.qvalues  = None
        
    def _initialize_smearer(self):
        """
        Initialize the C++ smearer object.
        This method HAS to be called before smearing
        """
        #self._smearer = smearer.new_q_smearer(numpy.asarray(self.width), self.min, self.max, self.nbins)
        self._smearer = smearer.new_q_smearer_with_q(numpy.asarray(self.width), self.qvalues)
        self._init_complete = True
        
    def get_unsmeared_range(self, q_min, q_max):
        """
        Determine the range needed in unsmeared-Q to cover
        the smeared Q range
        Take 3 sigmas as the offset between smeared and unsmeared space
        """
        # Range used for input to smearing
        _qmin_unsmeared = q_min
        _qmax_unsmeared = q_max 
        try:
            offset = 3.0*max(self.width)
            _qmin_unsmeared = max([self.min, q_min-offset])
            _qmax_unsmeared = min([self.max, q_max+offset])
        except:
            logging.error("_QSmearer.get_bin_range: %s" % sys.exc_value)
        return _qmin_unsmeared, _qmax_unsmeared
        
    
class QSmearer(_QSmearer):
    """
    Adaptor for Gaussian Q smearing class and SANS data
    """
    def __init__(self, data1D):
        """
        Assumption: equally spaced bins of increasing q-values.
        
        :param data1D: data used to set the smearing parameters
        """
        # Initialization from parent class
        super(QSmearer, self).__init__()
        
        ## Resolution
        self.width = numpy.zeros(len(data1D.x))
        if data1D.dx is not None and len(data1D.dx)==len(data1D.x):
            self.width = data1D.dx
        
        ## Number of Q bins
        self.nbins = len(data1D.x)
        ## Minimum Q 
        self.min = min(data1D.x)
        ## Maximum
        self.max = max(data1D.x)
        ## Q-values
        self.qvalues = data1D.x


if __name__ == '__main__':
    x = 0.001*numpy.arange(1,11)
    y = 12.0-numpy.arange(1,11)
    print x
    #for i in range(10): print i, 0.001 + i*0.008/9.0 
    #for i in range(100): print i, int(math.floor( (i/ (100/9.0)) )) 

    
    s = _SlitSmearer(nbins=10, width=0.0, height=0.005, min=0.001, max=0.010)
    #s = _QSmearer(nbins=10, width=0.001, min=0.001, max=0.010)
    s._compute_matrix()

    sy = s(y)
    print sy
    
    if True:
        for i in range(10):
            print x[i],y[i], sy[i]
            #print q, ' : ', s.weight(q), s._compute_iq(q) 
            #print q, ' : ', s(q), s._compute_iq(q) 
            #s._compute_iq(q)